Funding for innovation project on PET imaging
The UiT Machine Learning Group, together with collaborators at the PET center at the University Hospital of North Norway and the 180N Norwegian Nuclear Medicine Consortium, have been granted funding for an innovation project entitled "A novel AI-product for advancing tumor imaging with positron emission tomography". The aim of the project is to develop a new AI-product for PET imaging, and builds on many years of basic research and close collaboration between the Machine Learning Group and the University Hospital of North Norway. More information can be found here:
How is AI affecting democratic processes?
Visual Intelligence director Robert Jenssen gave a talk and participated in a panel debate organized by the Norwegian Academy of Science and Letters on the topic “How is AI affecting democratic processes?“.
Interview about new research
Ph.D. candidate Changkyu Choi from the UiT Machine Learning group was interviewed about his recent research article entitled "Semi-supervised target classification in multi-frequency echosounder data".
Digital Event on the Future of Research in Norway and Northern-Norway
Visual Intelligence director Robert Jenssen was one of the invited speakers to an exciting event hosted by UiT the Arctic University of Norway. The event invited top researchers to discuss the future of research in Norway and Northern-Norway in the next 10 years.
Recent News Article on the Newly Opened Norwegian Center for Clinical Artificial Intelligence
During the midnight sun conference, a new center for clinical artificial intelligence was opened. The Norwegian Center for Clinical Artificial Intelligence will, among other tasks, work closely with the UiT Machine Learning Group to bring research into the clinic.
Recent News Articles on PhD from UiT Machine Learning Group.
Rogelio Andrade Mancisidor recently defended his PhD, which has gained attenion from several sources. One recent news article highlights that Mancisidor was headhunted to a new position as Assistant Professor at BI before the thesis was even defended. Another news story underline that the methology put forth by Mancisidor might pave the way for a new way of conducting credit scoring. Mancisidor's PhD-project was a collaboration between the UiT Machine Learning Group and Santander Consumer Bank.
Recent News Article on Tromsø Advantages in AI Research
The development and research on AI of the UiT Machine Learning Group together with collaborators at the center for research-based innovation, Visual Intelligence, and the University Hospital of North Norway have been highlighted in a recent news article. The article also brings attention to the upcoming Midnight Sun Conference for Patient-Centered Artificial Intelligence conference that the UiT Machine Learning Group will be hosting together with a number of collaborators located in Northern Norway.
New Study Program on Artifical Intelligence
A new study program on artificial intelligence and machine learning will start in the autumn of 2021 at the University of Tromsø. The UiT Machine Learning Group will play a key role in teaching and guiding students that enroll in this study program.
UiT Machine Learning Group with new CVPR paper.
The UiT Machine Learning Group recently got a new paper accepted to the premier machine learning conference CVPR, entitled “Reconsidering Representation Alignment for Multi-view Clustering". The paper presents novel methods for identifying groups in data gathered through multiple views or with multiple modalities, using deep neural networks.
Funding for project on groundbreaking research in image analysis.
The UiT Machine Learning Group will receive funding for a new project aimed at developing groundbreaking algorithms for explainable image analysis based on deep learning. The project will be headed by associate professor Michael Kampffmeyer, and will have synergistic effects with the new center for research-based innovation Visual Intelligence.
Paper published at AAAI Conference on Artificial Intelligence 2021
New paper accepted to AAAI! The paper is a collaboration between NEC Labs, University of Florida, and UiT Machine Learning Group with first author Shujian Yu, and entitled "Measuring Dependence with Matrix-based Entropy Functional". AAAI is highly competitive, with 21% acceptance rate this year.
Advancing oncological PET imaging using machine learning
Algorithms with the capability to fuse information from different image modalities, such as PET, CT, and MRI, is of key importance in effective analysis of medical images. The UiT Machine Learning Group is heading one of the work packages in the 180° North project, which is tasked with developing novel methodology for extracting and combining information from different medical image modalities.
Paper published at European Conference on Computer Vison 202022nd September 2020
Our recent work entitled ”SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks” was accepted at the ECCV'20. The ECCV is a premier conference on machine learning where only the highest quality work is presented. The recent paper introduces a new dissimilarity measure designed for the task of few-shot classification, which aims at identifying unseen classes form just a handful of labeled examples.
Center for Research-based Innovation
The UiT Machine Learning Group will be heading a Center for Research-based Innovation, entitled Visual intelligence. Such a center is aimed at ensuring close collaboration between research and industry. The center will focus on developing cutting-edge technology for extracting information from images from many different domains such as medical and remote sensing imagery. The interested reader can find more information here, here and here.
National Collaboration for Machine Learning in Cancer Research
Machine learning is an important component of a national research collaboration ("Kystsamarbeidet"), where the UiT Machine Learning Group plays a key role. Recently the group have been working on new methodology for combining different modalities in medical imagining. The interested reader can find more information here and here.
Detecting Skin Cancer Using Hyperspectral Images
Skin cancer is one of the most common types of cancer, and in some parts of the world, it is becoming increasingly more common. Most skin cancers are not life-threatening, but one particular type called malignant melanoma is known to be quite deadly. The UiT Machine Learning Group have recently explored the state-of-the-art in the application of hyperspectral imaging for melanoma detection. The interested reader can read more here.
Leading in Health and AI
The UiT Machine Learning Group continues its fruitful collaboration with the University Hospital of North Norway, and has recently received funding for two research projects focused on the development of understandable decision support systems for medical application. The interested reader can find more information here and here.
Fake News Detection
The UiT Machine Learning Group has been awarded a Facebook research grant to develop algorithms that can automatically detect "fake news" and logical fallacy in online discourse. Postdoctoral researcher Jonas Nordhaug Myhre will, in collaboration with researchers from the University of Cambridge of and the University of Bergen, use natural language processing and machine learning techniques to develop such detectors. The interested reader can find more information here or here.
International Best Paper Award
Karl Øyvind Mikalsen, UiT Machine Learning Group, has been awarded best article within the field “computerized clinical decision support". The award was issued by the International Medical Informatics Society for the paper "Using anchors from free text in electronic health records to diagnose postoperative delirium". Interested readers can find the full article here.
Mikalsen's article also garnered attention from Norwegian media, which highlighted its potential for identifying patients with a higher risk of experiencing postoperative complications. The full article can be found here.